Learning Design Skills as Memory Policies for Agentic Photonic Inverse Design

2026-05-28 · Source: Computation and Language · Field: Science & Research — Engineering & Applied Sciences, Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences · Depth: Expert, quick

Summary

SkillPCF, a novel closed-loop agent framework, addresses the challenges of Photonic Crystal Fiber (PCF) inverse design by formulating it as a memory-policy learning problem. Traditional methods struggle to accumulate reusable design knowledge across iterative trials, especially given the coupled optical targets and expensive electromagnetic simulations involved. SkillPCF integrates a physics-guided memory skill bank, reinforcement-learned skill selection, and simulator-grounded skill evolution. The framework was evaluated using a real-world dataset comprising 479 expert interaction traces (2,507 spans) and 553 memory-dependent evaluation queries, covering dispersion engineering, loss optimization, and multi-objective design. Experiments demonstrated that SkillPCF achieves superior design-quality and efficiency trade-offs within practical simulation budgets compared to various LLM backbones and classical baselines, validating its memory-skill learning paradigm for physics-aware PCF inverse design.

Key takeaway

For Machine Learning Engineers or Research Scientists involved in photonic inverse design, SkillPCF offers a significant advancement. If you are struggling with accumulating design knowledge or managing expensive simulations, consider adopting a memory-policy learning approach. This framework demonstrates how combining physics-guided memory with reinforcement learning can achieve superior design quality and efficiency, enabling you to optimize complex optical systems more effectively within practical simulation budgets.

Key insights

SkillPCF uses memory-policy learning to accumulate design knowledge for efficient photonic inverse design.

Principles

• Accumulate reusable design knowledge across trials.
• Combine physics guidance with reinforcement learning.
• Formulate inverse design as memory-policy learning.

Method

SkillPCF combines a physics-guided memory skill bank, reinforcement-learned skill selection, and simulator-grounded skill evolution for closed-loop agentic photonic inverse design.

In practice

• Apply memory-policy learning to complex inverse design.
• Use SkillPCF for dispersion engineering and loss optimization.
• Improve design quality under simulation budgets.

Topics

• Photonic Inverse Design
• Memory-Policy Learning
• Reinforcement Learning
• SkillPCF Framework
• Photonic Crystal Fiber
• Electromagnetic Simulation

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.